code: def __init__(
      self,
      classes=1000,
      norm_layer=BatchNorm,
      norm_kwargs=None,
      **kwargs,
  ):
      super(ShuffleNetV2, self).__init__(**kwargs)
      with self.name_scope():
          self.features = nn.HybridSequential(prefix="")
          self.features.add(
              _conv2d(
                  channel=24,
                  kernel=3,
                  stride=2,
                  padding=1,
                  use_act=True,
                  use_bias=False,
              )
          )
          self.features.add(
              nn.MaxPool2D(pool_size=3, strides=2, padding=1)
          )
          self.features.add(self.make_stage(2))
          self.features.add(self.make_stage(3))
          self.features.add(self.make_stage(4))
          self.features.add(
              _conv2d(
                  channel=1024,
                  kernel=1,
                  stride=1,
                  use_act=True,
                  use_bias=False,
              )
          )
          self.features.add(nn.GlobalAvgPool2D())
          self.output = nn.Dense(classes)
  
imports: from mxnet.context import cpu
  from mxnet.gluon import nn
  from mxnet.gluon.nn import BatchNorm
  from mxnet.gluon.block import HybridBlock
label_desc: Use image node_id 1 to create a new ShuffleNetV2 object from inherited base classes: HybridBlock with example: obj = ShuffleNetV2(classes, norm_layer, norm_kwargs)